数据分析三剑客【AIoT阶段一(下)】(十万字博文 保姆级讲解)—Matplotlib—数据可视化进阶—Seaborn(3)(十六)

3.3.2.5 箱式图

import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
sns.set(style = 'ticks')
tips = pd.read_csv('./tips.csv')
ax = sns.boxplot(x = "day", y = "total_bill", data = tips, palette = 'colorblind')

数据分析三剑客【AIoT阶段一(下)】(十万字博文 保姆级讲解)—Matplotlib—数据可视化进阶—Seaborn(3)(十六)

3.3.2.6 直方图

import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
sns.set(style = 'dark')
x = np.random.randn(5000)
sns.histplot(x, kde = True)

import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
sns.set(style = 'darkgrid')
tips = pd.read_csv('./tips.csv')
sns.histplot(x = 'total_bill', data = tips, kde = True)

数据分析三剑客【AIoT阶段一(下)】(十万字博文 保姆级讲解)—Matplotlib—数据可视化进阶—Seaborn(3)(十六)

3.3.2.7 分类散点图

import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
sns.set(style = 'darkgrid')
exercise = pd.read_csv('./exercise.csv')
sns.catplot(x = "time", y = "pulse", hue = "kind", data = exercise)

数据分析三剑客【AIoT阶段一(下)】(十万字博文 保姆级讲解)—Matplotlib—数据可视化进阶—Seaborn(3)(十六)

3.3.2.8 热力图

import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
plt.figure(figsize = (12, 9))
flights = pd.read_csv('./flights.csv')   # 飞行数据
# pivot() 实现了数据重塑,改变了DataFrame的形状
# month 作为行索引,year作为列索引,passengers作为数据
flights = flights.pivot("month", "year", "passengers") # 年,月,乘客
sns.heatmap(flights, annot = True,   # 画上数值
            fmt = 'd',         # 数值为整数
            cmap = 'RdBu_r',   # 设置颜色
            linewidths = 0.5)  # 线宽为 0.5 

数据分析三剑客【AIoT阶段一(下)】(十万字博文 保姆级讲解)—Matplotlib—数据可视化进阶—Seaborn(3)(十六)

我们最后来说一下数据重塑,在本题的基础上,我们查看一下我们的flights 数据:

咋们再来重新加载一下数据,看看原始的flights 数据:

import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
flights = pd.read_csv('./flights.csv')
flights

数据分析三剑客【AIoT阶段一(下)】(十万字博文 保姆级讲解)—Matplotlib—数据可视化进阶—Seaborn(3)(十六)

不难看出,上述绘图过程中涉及到了数据重塑:代码:flights = flights.pivot("month", "year", "passengers")实现了数据的重塑,使得month 作为行索引,yerr 作为列索引,passengers 作为数据。